Python 'communities' Library: Implementing Louvain, Girvan‑Newman, Hierarchical, Spectral, and Bron‑Kerbosch Community Detection Algorithms

Network graphs consist of tightly connected nodes that can be grouped into clusters, known as community structures. Community detection algorithms aim to uncover these structures, and the Python library communities (available at GitHub ) implements a variety of such algorithms.

The library supports the following methods:

Louvain algorithm

Girvan‑Newman algorithm

Hierarchical clustering

Spectral clustering

Bron‑Kerbosch algorithm

It also provides visualization capabilities, demonstrated with the Zachary's karate club network.

Installation is straightforward via pip:

<code>pip install communities</code>

Example usage for the Louvain method:

<code>import numpy as np
from communities.algorithms import louvain_method

adj_matrix = np.array([
    [0, 1, 1, 0, 0, 0],
    [1, 0, 1, 0, 0, 0],
    [1, 1, 0, 1, 0, 0],
    [0, 0, 1, 0, 1, 1],
    [0, 0, 0, 1, 0, 1],
    [0, 0, 0, 1, 1, 0]
])

communities, _ = louvain_method(adj_matrix)
print(communities)  # [{0, 1, 2}, {3, 4, 5}]
</code>

Similar code snippets are provided for the other algorithms (Girvan‑Newman, hierarchical clustering, spectral clustering, and Bron‑Kerbosch), each showing how to import the function, supply an adjacency matrix, and obtain the detected communities.

Visualization functions such as draw_communities and louvain_animation generate colored graphs and animated demonstrations of community evolution.

Overall, the communities library offers a comprehensive toolkit for researchers and developers working on graph‑based community detection and network visualization in Python.